Size Of Battery Energy Storage System Research Articles

Optimizing Energy Management and Sizing of Photovoltaic Batteries for a Household in Granada, Spain: A Novel Approach Considering Time Resolution

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low‐inertia grids

AbstractAs inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.

Enhanced Frequency Containment Reserve provision from battery hybridized hydropower plants: Theory and experimental validation

This paper presents a solution to address wear and tear of Run-of-River (RoR) Hydropower Plants (HPPs) providing enhanced Frequency Containment Reserve (FCR). In this respect, the study proposes the integration of a Battery Energy Storage System (BESS) with RoR HPPs controlled by a double-layer Model Predictive Control (MPC). The upper layer MPC acts as a state of energy manager for the BESS, employing a forecast of the required regulating energy for the next hour. The lower-layer MPC optimally allocates the power set-point between the turbine and the BESS. Reduced-scale experiments are performed on a one-of-a-kind testing platform to validate the proposed MPC-based control considering a comparison with different control strategies and different BESS sizes. The results demonstrate superior performance of the proposed framework, compared to simpler techniques like dead-band control or to the standalone RoR scenario, leading to improved FCR provision, reduced servomechanism stress, and extended hydropower asset lifespan.

Techno-economic simulation and sensitivity analysis of modular cogeneration with organic rankine cycle and battery energy storage system for enhanced energy performance

This paper presents a comprehensive evaluation of the efficiency and cost implications of a modular cogeneration plant integrated with a Battery Energy Storage System (BESS) and an Organic Rankine Cycle (ORC) plant. The objective is to assess the performance of the system when waste heat recovery through the ORC cycle is added to the exhaust line and compare it with the original system. Techno-economic parameters were analyzed for two different configurations: one with the maximum BESS size and another with the maximum Primary Energy Savings (PES).The results demonstrate that the incorporation of the ORC plant yields several notable outcomes. While there is a slight increase in the Simple Payback Period (SPB) and Net Present Value (NPV) in both configurations, the magnitudes of these increases are relatively low. In contrast, the introduction of the ORC cycle leads to substantial reductions in carbon dioxide (CO2) emissions and significant improvements in Primary Energy Savings (PES). For the Max PES configuration, a remarkable 25% reduction in CO2 emissions and a substantial 22% improvement in PES were observed. To further investigate the system's response to varying economic conditions, a sensitivity analysis was performed. The analysis considered the effects of changes in the cost of battery, Combined Heat and Power (CHP) unit, ORC components, electricity, and fuel on the system's Simple Payback Period (SPB) and Net Present Value (NPV). The results revealed that the sensitivity of the Max PES configuration to economic parameters was higher compared to the Max BESS system.These findings highlight the considerable advantages of integrating an ORC plant into a cogeneration system with a BESS. The significant reduction in CO2 emissions and the noteworthy improvement in Primary Energy Savings (PES) outweigh the relatively minor negative impact on the Simple Payback Period (SPB) and Net Present Value (NPV). This integrated configuration demonstrates both environmental benefits and energy efficiency improvements. The results provide valuable insights for the design, optimization, and decision-making processes involved in the implementation of cogeneration systems with energy storage, promoting sustainable and cost-effective solutions.

Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook

This paper proposes a method to assess the financial attractiveness provided by adding a Battery Energy Storage System (BESS) in distributed photovoltaic (PV) generation on public buildings in Brazil. The method is applicable to prosumer units (PU) connected on the medium voltage grid operating under time-based electricity tariffs. The BESS primary objective was to achieve optimal reductions in electric energy expenses through effective energy arbitrage mechanisms. The procedural steps of the methodology begin with an analysis encompassing assessment of solar resource, PU consumption profile, and BESS sizing and operation. Subsequently, steps entail contracted power optimization, PU net-metering analysis, and evaluation of BESS impacts on electric energy expenses. Lastly, a regulatory and economic analysis is conducted incorporation considerations on BESS taxation, behind-the-meter regulations, and a sensitivity assessment extending to the 2030 outlook. By 2050, it is anticipated that PV generation will surpass hydropower, becoming the predominant component of Brazil's energy mix. This shift is expected to create an increased need for solutions, notably storage systems, capable of meeting flexibility requirements and maintaining grid resilience. The results suggest that the financial viability of incorporating BESS becomes favourable when the battery cost is below 365 $/kWh. In approximately 50% of the Brazilian territory, prevailing economic conditions support the adoption of BESS. Nationwide feasibility of integration is anticipated for the year 2027. It was observed that government policies to exempt BESS taxation, even if temporary, would be extremely interesting to promote the widespread adoption of this technology. The 2030 outlook of the transition to these benign renewable energy technologies is already in place, and will dominate the energy mix.

Frequency Stability Constrained BESS Sizing Model for Microgrids

The grid integration of renewable energy sources necessitates using energy storage systems (ESSs) to provide more flexibility and controllability. This paper proposes a frequency stability-constrained battery energy storage system (BESS) sizing model for microgrids formulated as a mixed-integer linear programming (MILP) problem and decomposed using Benders decomposition. The optimal size of BESS is determined as a trade-off between minimizing the operating costs or maximizing the benefits and the high investment costs of BESS. Both the grid-connected and stand-alone operating modes are modeled for the microgrid along with the corresponding generation contingencies. The microgrid scheduling optimization model is built for cost-benefit analysis considering unit commitment and frequency stability (FS) constraints. The transient frequency dynamics are considered using an accurate time-domain model based on the discretized swing equation. The proposed method ensures frequency stability criteria, such as frequency nadir/overshoot, rate of change of frequency (RoCoF), and steady-state frequency, are within their allowable ranges following generation contingencies. Simulation results based on a medium-voltage microgrid test case with historical data of load, PV generation, and market price verify the effectiveness of the proposed method.

On sizing of battery energy storage systems for PV plants power smoothing

The high variability of solar irradiance causes fluctuations in the generation of photovoltaic (PV) power plants. This characteristic affects power system operation, thus, strategies are necessary to mitigate the intermittent characteristics of PV power plants. Several countries adopt grid codes incorporating ramp rate (RR) limitations for PV power injection into the grid, also known as power smoothing control. In this sense, battery energy storage systems (BESS) with coordinated RR control algorithms are commonly applied to mitigate current fluctuations from the PV system to the grid. Therefore, studies on the effect of local solar irradiance on BESS requirements for PV power smoothing applications are very important. It is also worth highlighting that in the literature there is a gap in the evaluation of aspects of battery lifetime, BESS volume, price and BESS oversizing for selecting an ideal battery model, among a catalog of batteries, constituting an industrial concern. Therefore, this work presents a detailed BESS sizing methodology, evaluating aspects of the energy requirements, power, and discharging rate (C-rate), based on the mission profile (featuring a customized solution for each project). In addition, the effect of the battery temperature control on the BESS sizing is evaluated. The case study is based on a 1 MW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânia-Brazil. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10%/min. The BESS sizing is evaluated in terms of BESS volume, lifetime, and oversizing factor for power and energy. The proposed methodology reduces the BESS volume by 57.14% in relation to the traditional BESS design of literature.

Battery Energy Storage System Planning Based on Super-Resolution Source-Load Uncertainty Reconstruction

Low-time resolution electricity data have been used to drive battery energy storage system (BESS) planning due to data barriers. However, the coarse-resolution time series cannot reflect real power variation, and the planning results may be inappropriate due to the unrealistic representation of source-load uncertainties. To this end, this paper proposes a BESS planning method based on super-resolution (SR) source-load uncertainty reconstruction. The Gramian angular field (GAF) is employed to transform the 1D time series into 2D images to enrich the time dependency of signals. With advanced computer vision (CV), the entropy decrease generative adversarial network (EDGAN) based on information-growth attention is designed to recover the source-load profile from low resolution to high resolution. To depict source-load uncertainties in BESS sizing, a two-stage multi-distributionally robust optimization (2S-MDRO) is established under SR ambiguity sets. Case studies validated the accuracy of the proposed SR method and proved the effectiveness of the proposed sizing model. The recommended time resolution for BESS planning is presented in the comparative results.

Large-Scale BESS for Damping Frequency Oscillations of Power Systems with High Wind Power Penetration

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid.

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties

Battery energy storage systems (BESS) are integrated with renewable distribution generators (DG) within the distribution network (DN) to mitigate active power loss and improve the bus voltage profile through optimal placement and sizing. This work identifies the optimal location for BESS and DGs placement by deriving a loss sensitivity factor by considering the power flow through the branches and bus losses. With optimal location, a novel approach for selecting an optimal charging/discharging time schedule is proposed. This method aims to determine the optimal size and scheduling of BESS through the minimization of the voltage deviation and real power loss in the DN. Following the installation of BESS, the wind and solar DG optimal sizes are determined at maximum power generating hours. To address the uncertainties present in DN, Hong’s (2m+1) point estimation method is utilized to optimize the network operation incorporated with BESS and DGs. Furthermore, a techno-economic analysis is conducted to examine the energy savings resulting from integrating DGs and BESS in the DN. The power losses are further reduced by implementing network reconfiguration to reduce the energy dependency on the grid. The effectiveness of the proposed approach is tested on a 33-bus, 69-bus, and 118-bus DN under seasonal load variations. Incorporating the renewable DG with BESS into the DN reduces the grid energy supply by 30%–40%. The results of this study pave the way for more sustainable and efficient power systems by contributing to the development of techniques for integrating renewable energy sources into DNs.

A novel virtual inertia-based damping stabilizer for frequency control enhancement for islanded microgrid

The Inverter-based generator lacks natural inertia, which leads to instability problems in Microgrid frequency. Virtual inertia is a promising control concept that rapidly injects/absorbs active power to/from the power system to ensure the frequency stability of low-inertia systems. This paper proposes an innovative virtual damping stabilizer (VDS) integrated into battery energy storage system (BESS) based virtual inertia and damping control loops. The proposed VDS adds supplementary damping and stabilizing capability to the BESS by properly correcting and controlling its inertia pumped/drawn power. The VDS aims to enhance frequency stability, decrease the necessary BESS large capacity, and lower the initial expense. Furthermore, the proposed VDS helps operate the BESS efficiently by smoothing the output power profile during transient disturbances, consequently elongating the lifetime. The Particle Swarm Optimization (PSO) method is utilized to get the optimal parameters of the proposed VDS. A frequency control model of an isolated microgrid with the proposed controller is presented, considering various operating conditions. Time-domain simulations and experimental validation using OPAL-RT verify the efficacy of the proposed VDS scheme. Sensitivity and eigenvalues analyses assess the VDS's dynamic stability enhancement and robustness against inherent uncertainties in Microgrid parameters. The results demonstrate a superior frequency response and a reduction in the BESS size compared to PSO and model predictive controller (MPC) based virtual inertia and damping strategies.

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Optimal BESS Sizing for Industrial Facilities Participating in RTP DR

The predictability of their manufacturing lines allows industrial facilities to optimize their production scheduling and to participate in demand response (DR), in day-ahead, real-time pricing (RTP) electricity markets. Battery energy storage systems (BESSs) make the electrical demand of industrial facilities more flexible and increase their potential to benefit from DR. The BESS sizing problem, for industrial facilities participating in RTP DR, is complex due to the discreteness of their manufacturing lines and the stochastic nature of electricity pricing. In this paper, an approach to BESS sizing is proposed. Scenario extraction using k-means clustering is used to reduce the problem complexity, and the extracted scenarios are preprocessed to reduce the search space for the optimal size of the BESS. The steps involved in the proposed approach are demonstrated, in detail, through a case study that uses a generic model of an industrial unit. The results of the case study show the effectiveness and validity of the problem reduction techniques used and highlight the role of electricity storage in maximizing the profits of the industrial unit. Finally, a sensitivity analysis is carried out to illustrate the impact of the BESS installation cost on the results.

Management of electric vehicle charging stations in low-voltage distribution networks integrated with wind turbine–battery energy storage systems using metaheuristic optimization

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved.

Cost-Effective Size of HESS Incorporated in VSG With Enhanced Performance of Battery Providing Implicit Inertia

Virtual synchronous generator (VSG) incorporates hybrid energy storage system (HESS) comprising supercapacitor (SC) and battery energy storage system (BESS) for frequency support services (FSS). In this work, new aspects namely implicit contribution of BESS in inertia and subjection of higher magnitude stress factors (SFs) on BESS during FSS are observed, while HESS is sized by existing methodology. Least square estimation and novel performance evaluation model are used to quantify the BESS implicit contribution in inertia and decrease in its desirable performance due to higher SFs, respectively. The resulting computations are utilized in reducing the SC size and marginally increasing the BESS size that leads to reduction in SFs on it. The estimated HESS size also demonstrates lower investment cost (INVC). Further, the implicit contribution by BESS in inertia and reduction in SFs with its estimated size is validated at Power Hardware-In-Loop (PHIL) level.

Use of Physics-Based Models for Different Applications of Electrochemical Energy Storage and Conversion Devices

This talk will present the application of physics-based models applied to two different electrochemical storage and conversion systems, viz. Li-ion batteries and Proton Exchange Membrane Fuel Cells (PEMFCs). Battery energy storage system (BESS) is becoming a crucial part of standalone renewable hybrid power systems due to the intermittent nature of power generation and for aiding in various operations like frequency regulation, voltage support, and peak shaving. The lithium-ion battery is a promising choice for BESS due to its high energy density, power density, operating voltage, negligible self-discharge, suitable operating temperature range, modularity, and reliability. A standalone renewable hybrid power system framework with lithium-ion battery energy storage is developed to investigate its performance using physics-based battery models. Power management and control strategies are developed for standalone renewable hybrid power systems. These control strategies can track the MPP of the solar cells and wind turbines while avoiding overcharging the battery and guaranteeing 0% dumping power under different ambient and working conditions. The battery is exposed to harsh ambient conditions in the real world, resulting in the dynamic and continuous capacity fade. A thermal management and control strategy is developed to analyze the effect of temperature on lithium-ion batteries' performance and degradation without using external cooling systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of renewable hybrid power systems. The impact of BESS size variation on degradation and the cost of energy generation is analyzed. The developed renewable hybrid power system framework is independent of location and system size and can be extended to incorporate other renewable energy generation sources and energy storage systems.The second part of this talk will focus on using physics-based models to understand the water dynamics and degradation mechanisms inside PEMFC, which is necessary for water management, long-life operation, and cost reduction. PEMFCs have emerged as an alternative green energy technology in various applications such as automobiles, portable and stationary applications, and auxiliary power supplies for space exploration missions. The cost and durability targets remain unmet largely, which restrains the commercialization of the PEMFCs. The major difficulty in modeling lies in the wide scale of dimensions from the millimeter level (flow channel) to the micron level (catalyst layer (CL) thickness). The multi-scale physics-based model enables the life cycle analysis of PEMFC under various operating conditions. The mass transport limitation is the major hurdle in performance enhancement for higher current density operations. It arises from the liquid water generated during the electrochemical reactions, which hinders the reaction sites of CL and oxygen transport reaction sites in the gas diffusion layer, resulting in performance deterioration. Many experimental studies reported the two-phase flow visualization techniques in PEMFCs, viz. neutron imaging, optical visualization, and X-ray radiography. However, the difficulties in conducting experiments for flow visualization and related parameters such as liquid volume fraction have encouraged researchers to adopt numerical simulations that are less expensive and computationally efficient. Understanding liquid water movement inside the cell at the micro-scale help to improve the PEMFC performance at the stack level. Here, we develop a comprehensive 3-D, multiphase, non-isothermal, steady-state physics-based model of the PEMFC that can closely approximate the liquid water flooding and membrane drying. The findings from this work can be used to design an efficient flow configuration and to study membrane deformation due to the shrinking-swelling under humidification and thermal cycling. The ultimate goal of the multi-scale model is to enhance the PEMFC stack output with less degradation.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.